We will apply our novel paradigm for treating the long time dynamics of flexible peptides and proteins to describe the dynamics of several biosystems, including the unfolding of a model beta-barrel protein. In addition, we will continue to test and enhance the applicability and efficiency of the protein. In addition. we will continue to test and enhance the applicability and efficiency of the theory. This theory is designed to describe the dynamics of peptides with no single native structure, proteins with flexible portions, relative motions of domains in proteins, and the unfolding of proteins. A primed example of a peptide lacking a single native structure is the amyloid beta-peptide, involved in plaque formation occurring with Alzheimer's disease, which may adopt random coil-, khi- helical, or beta-sheet structures (the latter promotes plaque formation). The goal of the theory is to describe dynamics of time scales orders of magnitude longer than those accessible to molecular describe dynamics on time scales orders of magnitude longer than those accessible to molecular dynamics (MD) stimulations. Applications will consider neurotransmitters, such as met-enkephalin, and endothelins which are associated with hypertension, renal failure, myocardial infarction, etc., and the unfolding of a beta-barrel protein. Comparisons with MD simulations for all these systems will provide unambiguous (no adjustable parameters) tests of the theory and will enable its refinement. The theory merges general principles of reduced descriptions in statistical mechanics with hydrodynamic models for the solvent dynamics, and with the scrupulous testing and improvement of all assumptions. Computations will be closely coordinated with planned experimental probes of peptide dynamics by Scherer (Chicago) using single molecule spectroscopy and femtosecond infrared pump-probe anisotropy methods. We will also predict NMR experiments and especially consider cases where discrepancies exist between different NMR data and/or between NMR and X-ray data.